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Global Data Science Compensation Trends
Data Science and Analytics
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About
This dataset provides a detailed categorisation of salaries within various data science fields. It offers valuable insights into compensation trends, roles, and employment conditions across different experience levels, company sizes, and geographical locations for the years 2020, 2021, and 2022. It is ideal for understanding the financial landscape of the data science domain and for benchmarking salaries.
Columns
- ID: A unique identifier for each record.
- Working Year: The year the salary was paid (2020, 2021, 2022).
- Designation: The role worked in during the year, such as Data Scientist or Data Engineer. The dataset includes 50 unique designations.
- Experience: The experience level for the job, categorised as Entry-level/Junior (EN), Mid-level/Intermediate (MI), Senior-level/Expert (SE), and Executive-level/Director (EX).
- Employment Status: The type of employment for the role, including Part-time (PT), Full-time (FT), Contract (CT), and Freelance (FL).
- Salary In Rupees: The total gross salary amount paid.
- Employee Location: The employee's primary country of residence during the work year, represented as an ISO 3166 country code. There are 57 unique employee locations.
- Company Location: The country of the employer's main office or contracting branch. The dataset contains 50 unique company locations.
- Company Size: The median number of people who worked for the company during the year, categorised as Small (S - Less than 50 employees), Medium (M - 50 to 250 employees), and Large (L - More than 250 employees).
- Remote Working Ratio: The overall amount of work done remotely, indicating No Remote Work (0 - less than 20%), Partially Remote (50), or Fully Remote (100 - more than 80%).
Distribution
The dataset is typically provided in a CSV format and is approximately 36.44 kB in size. It contains 607 records across 10 distinct columns. Each column has full data availability with no missing values.
Usage
This dataset is ideal for:
- Salary benchmarking for data science roles.
- Analysing career progression and salary growth across experience levels.
- Understanding geographical salary variations for data science professionals.
- Investigating the impact of company size and remote work policies on compensation.
- Identifying trends in employment status within the data science domain.
Coverage
- Time Range: The data spans three years: 2020, 2021, and 2022.
- Geographic Scope: Includes employee and company locations from a diverse set of countries, with the United States and Great Britain being the most frequent locations. It leverages ISO 3166 country codes for location representation.
- Professional Scope: Covers various data science designations, experience levels from junior to executive, and different employment types. Data is available for companies of small, medium, and large sizes, and categorises remote work ratios.
License
CC0: Public Domain
Who Can Use It
- Data Scientists and Analysts: For salary negotiation, career planning, and market research.
- Human Resources Professionals: To benchmark salaries, develop compensation strategies, and attract talent.
- Job Seekers: To understand expected salary ranges for different roles and experience levels.
- Researchers and Academics: For studies on labour market trends in the technology sector.
- Policy Makers: To inform decisions related to skill development and economic policy.
Dataset Name Suggestions
- Data Science Salary Insights 2020-2022
- Global Data Science Compensation Trends
- AI/ML Job Market Salaries
- Data Professional Salary Benchmarking
- Tech Industry Salaries Overview
Attributes
Original Data Source: Global Data Science Compensation Trends
QUALITY